Trees "A tree may grow a thousand feet tall, but its leaves will return to its roots." -Chinese Proverb CLRS, Section 10.4.

Trees "A tree may grow a thousand feet tall, but its leaves will return to its roots." -Chinese Proverb CLRS, Section 10.4

What is a Tree? In computer science, a tree is a implementation-dependent data structure representing a hierarchical structure. A tree consists of nodes with a parent-child relation. Applications: Organization charts File systems Programming environments Computers”R”Us Sales R&D Manufacturing Laptops Desktops US International Europe Asia Canada CS Data Structures

Tree Terminology Root: node without parent (A)
Internal node: node with at least one child (A, B, C, F) External node (a.k.a. leaf ): node without children (E, I, J, K, G, H, D) Ancestors of a node: parent, grandparent, grand-grandparent, etc. Depth of a node: number of ancestors Height of a tree: maximum depth of any node (3) Descendant of a node: child, grandchild, etc. A B D C G H E F I J K sub-tree Sub-tree: tree consisting of a node and its descendants, grandchildren, etc. CS Data Structures

Tree Traversal A tree traversal visits the nodes of a tree in a systematic manner. Four types of traversal: Pre-order Post-order In-order Level-order pre-order post-order in-order level-order CS Data Structures

Pre-order Traversal In a pre-order traversal, a node is visited before its descendants. Application: Print a structured document. Algorithm preOrder(v) visit(v) for each child w of v preOrder (w) Make Money Fast! 1. Motivations References 2. Methods 2.1 Stock Fraud 2.2 Ponzi Scheme 1.1 Greed 1.2 Avidity 2.3 Bank Robbery 1 2 3 5 4 6 7 8 9 CS Data Structures

Pre-order Traversal 1 2 3 5 4 6 7 8 9 Make Money Fast! 1. Motivations
References 2. Methods 2.1 Stock Fraud 2.2 Ponzi Scheme 1.1 Greed 1.2 Avidity 2.3 Bank Robbery 1 2 3 5 4 6 7 8 9 Algorithm preOrder(v) visit(v) for each child w of v preOrder (w) preOrder(Make Money Fast!) visit(Make Money Fast!) // 1 preOrder(1. Motivations) // left child CS Data Structures

References 2. Methods 2.1 Stock Fraud 2.2 Ponzi Scheme 1.1 Greed 1.2 Avidity 2.3 Bank Robbery 1 2 3 5 4 6 7 8 9 Algorithm preOrder(v) visit(v) for each child w of v preOrder (w) preOrder(1. Motivations) visit(1. Motivations) // 2 preOrder(1.1 Greed) // left child CS Data Structures

References 2. Methods 2.1 Stock Fraud 2.2 Ponzi Scheme 1.1 Greed 1.2 Avidity 2.3 Bank Robbery 1 2 3 5 4 6 7 8 9 Algorithm preOrder(v) visit(v) for each child w of v preOrder (w) preOrder(1.1 Greed) visit(1.1 Greed) // 3 return // no children CS Data Structures

References 2. Methods 2.1 Stock Fraud 2.2 Ponzi Scheme 1.1 Greed 1.2 Avidity 2.3 Bank Robbery 1 2 3 5 4 6 7 8 9 Algorithm preOrder(v) visit(v) for each child w of v preOrder (w) preOrder(1. Motivation) preorder(1.2 Avidity) // right child CS Data Structures

References 2. Methods 2.1 Stock Fraud 2.2 Ponzi Scheme 1.1 Greed 1.2 Avidity 2.3 Bank Robbery 1 2 3 5 4 6 7 8 9 Algorithm preOrder(v) visit(v) for each child w of v preOrder (w) preOrder(1.2 Avidity) visit(1.2 Avidity) // 4 return // no children CS Data Structures

References 2. Methods 2.1 Stock Fraud 2.2 Ponzi Scheme 1.1 Greed 1.2 Avidity 2.3 Bank Robbery 1 2 3 5 4 6 7 8 9 Algorithm preOrder(v) visit(v) for each child w of v preOrder (w) preOrder(1. Motivations) return // all children visited CS Data Structures

References 2. Methods 2.1 Stock Fraud 2.2 Ponzi Scheme 1.1 Greed 1.2 Avidity 2.3 Bank Robbery 1 2 3 5 4 6 7 8 9 Algorithm preOrder(v) visit(v) for each child w of v preOrder (w) preOrder(Make Money Fast!) preOrder(2. Methods) // middle child CS Data Structures

References 2. Methods 2.1 Stock Fraud 2.2 Ponzi Scheme 1.1 Greed 1.2 Avidity 2.3 Bank Robbery 1 2 3 5 4 6 7 8 9 Algorithm preOrder(v) visit(v) for each child w of v preOrder (w) preOrder(2. Methods) visit(2. Methods) // 5 preOrder(2.1 Stock Fraud) // left child CS Data Structures

References 2. Methods 2.1 Stock Fraud 2.2 Ponzi Scheme 1.1 Greed 1.2 Avidity 2.3 Bank Robbery 1 2 3 5 4 6 7 8 9 Algorithm preOrder(v) visit(v) for each child w of v preOrder (w) preOrder(2.1 Stock Fraud) visit(2.1 Stock Fraud) // 6 return // no children CS Data Structures

References 2. Methods 2.1 Stock Fraud 2.2 Ponzi Scheme 1.1 Greed 1.2 Avidity 2.3 Bank Robbery 1 2 3 5 4 6 7 8 9 Algorithm preOrder(v) visit(v) for each child w of v preOrder (w) preOrder(2. Methods) preOrder(2.2 Ponzi Scheme) // middle child CS Data Structures

References 2. Methods 2.1 Stock Fraud 2.2 Ponzi Scheme 1.1 Greed 1.2 Avidity 2.3 Bank Robbery 1 2 3 5 4 6 7 8 9 Algorithm preOrder(v) visit(v) for each child w of v preOrder (w) preOrder(2.2 Ponzi Scheme) visit(2.2 Ponzi Scheme) // 7 return // no children CS Data Structures

References 2. Methods 2.1 Stock Fraud 2.2 Ponzi Scheme 1.1 Greed 1.2 Avidity 2.3 Bank Robbery 1 2 3 5 4 6 7 8 9 Algorithm preOrder(v) visit(v) for each child w of v preOrder (w) preOrder(2. Methods) preOrder(2.3 Bank Robbery) // right child CS Data Structures

References 2. Methods 2.1 Stock Fraud 2.2 Ponzi Scheme 1.1 Greed 1.2 Avidity 2.3 Bank Robbery 1 2 3 5 4 6 7 8 9 Algorithm preOrder(v) visit(v) for each child w of v preOrder (w) preOrder(2.3 Bank Robbery) visit(2.3 Bank Robbery) // 8 return // no children CS Data Structures

References 2. Methods 2.1 Stock Fraud 2.2 Ponzi Scheme 1.1 Greed 1.2 Avidity 2.3 Bank Robbery 1 2 3 5 4 6 7 8 9 Algorithm preOrder(v) visit(v) for each child w of v preOrder (w) preOrder(2. Methods) return // all children visited CS Data Structures

References 2. Methods 2.1 Stock Fraud 2.2 Ponzi Scheme 1.1 Greed 1.2 Avidity 2.3 Bank Robbery 1 2 3 5 4 6 7 8 9 Algorithm preOrder(v) visit(v) for each child w of v preOrder (w) preOrder(Make Money Fast!) preOrder(3. References) // right child CS Data Structures

References 2. Methods 2.1 Stock Fraud 2.2 Ponzi Scheme 1.1 Greed 1.2 Avidity 2.3 Bank Robbery 1 2 3 5 4 6 7 8 9 Algorithm preOrder(v) visit(v) for each child w of v preOrder (w) preOrder(3. References) return // no children CS Data Structures

References 2. Methods 2.1 Stock Fraud 2.2 Ponzi Scheme 1.1 Greed 1.2 Avidity 2.3 Bank Robbery 1 2 3 5 4 6 7 8 9 Algorithm preOrder(v) visit(v) for each child w of v preOrder (w) preOrder(Make Money Fast!) return // all children visited CS Data Structures

References 2. Methods 2.1 Stock Fraud 2.2 Ponzi Scheme 1.1 Greed 1.2 Avidity 2.3 Bank Robbery 1 2 3 5 4 6 7 8 9 Algorithm preOrder(v) visit(v) for each child w of v preOrder (w) preOrder(Make Money Fast!) Done. CS Data Structures

Post-order Traversal In a post-order traversal, a node is visited after its descendants. Application: Compute space used by files in a directory and its sub-directories. Algorithm postOrder(v) for each child w of v postOrder (w) visit(v) 9 cs16/ 8 3 7 todo.txt 1K homeworks/ programs/ 1 2 4 5 6 h1c.doc 3K h1nc.doc 2K DDR.java 10K Stocks.java 25K Robot.java 20K CS Data Structures

Post-order Traversal 9 3 1 7 2 4 5 6 8 cs16/ homeworks/ todo.txt 1K
programs/ DDR.java 10K Stocks.java 25K h1c.doc 3K h1nc.doc 2K Robot.java 20K 9 3 1 7 2 4 5 6 8 Algorithm postOrder(v) for each child w of v postOrder (w) visit(v) postOrder(cs16/) postOrder(homeworks/) // left child CS Data Structures

programs/ DDR.java 10K Stocks.java 25K h1c.doc 3K h1nc.doc 2K Robot.java 20K 9 3 1 7 2 4 5 6 8 Algorithm postOrder(v) for each child w of v postOrder (w) visit(v) postOrder(homeworks/) postOrder(h1c.doc/) // left child CS Data Structures

programs/ DDR.java 10K Stocks.java 25K h1c.doc 3K h1nc.doc 2K Robot.java 20K 9 3 1 7 2 4 5 6 8 Algorithm postOrder(v) for each child w of v postOrder (w) visit(v) postOrder(h1c.doc/) // no children visit(h1c.doc) // 1 CS Data Structures

programs/ DDR.java 10K Stocks.java 25K h1c.doc 3K h1nc.doc 2K Robot.java 20K 9 3 1 7 2 4 5 6 8 Algorithm postOrder(v) for each child w of v postOrder (w) visit(v) postOrder(homeworks/) postOrder(h1nc.doc/) // right child CS Data Structures

programs/ DDR.java 10K Stocks.java 25K h1c.doc 3K h1nc.doc 2K Robot.java 20K 9 3 1 7 2 4 5 6 8 Algorithm postOrder(v) for each child w of v postOrder (w) visit(v) postOrder(h1nc.doc/) // no children visit(h1nc.doc) // 2 CS Data Structures

programs/ DDR.java 10K Stocks.java 25K h1c.doc 3K h1nc.doc 2K Robot.java 20K 9 3 1 7 2 4 5 6 8 Algorithm postOrder(v) for each child w of v postOrder (w) visit(v) postOrder(homeworks/) // all children visited visit(homeworks/) // 3 CS Data Structures

programs/ DDR.java 10K Stocks.java 25K h1c.doc 3K h1nc.doc 2K Robot.java 20K 9 3 1 7 2 4 5 6 8 Algorithm postOrder(v) for each child w of v postOrder (w) visit(v) postOrder(cs16/) postOrder(programs/) // middle child CS Data Structures

programs/ DDR.java 10K Stocks.java 25K h1c.doc 3K h1nc.doc 2K Robot.java 20K 9 3 1 7 2 4 5 6 8 Algorithm postOrder(v) for each child w of v postOrder (w) visit(v) postOrder(programs/) postOrder(DDR.java/) // left child CS Data Structures

programs/ DDR.java 10K Stocks.java 25K h1c.doc 3K h1nc.doc 2K Robot.java 20K 9 3 1 7 2 4 5 6 8 Algorithm postOrder(v) for each child w of v postOrder (w) visit(v) postOrder(DDR.java) // no children visit(DDR.java) // 4 CS Data Structures

programs/ DDR.java 10K Stocks.java 25K h1c.doc 3K h1nc.doc 2K Robot.java 20K 9 3 1 7 2 4 5 6 8 Algorithm postOrder(v) for each child w of v postOrder (w) visit(v) postOrder(programs/) postOrder(Stocks.java/) // middle child CS Data Structures

programs/ DDR.java 10K Stocks.java 25K h1c.doc 3K h1nc.doc 2K Robot.java 20K 9 3 1 7 2 4 5 6 8 Algorithm postOrder(v) for each child w of v postOrder (w) visit(v) postOrder(Stocks.java) // no children visit(Stock.java) // 5 CS Data Structures

programs/ DDR.java 10K Stocks.java 25K h1c.doc 3K h1nc.doc 2K Robot.java 20K 9 3 1 7 2 4 5 6 8 Algorithm postOrder(v) for each child w of v postOrder (w) visit(v) postOrder(programs/) postOrder(Robot.java/) // right child CS Data Structures

programs/ DDR.java 10K Stocks.java 25K h1c.doc 3K h1nc.doc 2K Robot.java 20K 9 3 1 7 2 4 5 6 8 Algorithm postOrder(v) for each child w of v postOrder (w) visit(v) postOrder(Robot.java) // no children visit(Robot.java) // 6 CS Data Structures

programs/ DDR.java 10K Stocks.java 25K h1c.doc 3K h1nc.doc 2K Robot.java 20K 9 3 1 7 2 4 5 6 8 Algorithm postOrder(v) for each child w of v postOrder (w) visit(v) postOrder(programs/) // all children visited visit(programs/) // 7 CS Data Structures

programs/ DDR.java 10K Stocks.java 25K h1c.doc 3K h1nc.doc 2K Robot.java 20K 9 3 1 7 2 4 5 6 8 Algorithm postOrder(v) for each child w of v postOrder (w) visit(v) postOrder(cs16/) postOrder(todo.txt) // right child CS Data Structures

programs/ DDR.java 10K Stocks.java 25K h1c.doc 3K h1nc.doc 2K Robot.java 20K 9 3 1 7 2 4 5 6 8 Algorithm postOrder(v) for each child w of v postOrder (w) visit(v) postOrder(todo.txt) // no children visit(todo.txt) // 8 CS Data Structures

programs/ DDR.java 10K Stocks.java 25K h1c.doc 3K h1nc.doc 2K Robot.java 20K 9 3 1 7 2 4 5 6 8 Algorithm postOrder(v) for each child w of v postOrder (w) visit(v) postOrder(cs16/) // all children visited visit(cs16/) // 9 CS Data Structures

programs/ DDR.java 10K Stocks.java 25K h1c.doc 3K h1nc.doc 2K Robot.java 20K 9 3 1 7 2 4 5 6 8 Algorithm postOrder(v) for each child w of v postOrder (w) visit(v) Done. CS Data Structures

In-order Traversal In an in-order traversal a node is visited after its left sub-tree and before its right sub-tree. Applies to Binary Trees only. Application: Draw a binary tree. Algorithm inOrder(v) if left (v) ≠ null inOrder (left (v)) visit(v) if right(v) ≠ null inOrder (right (v)) 3 1 2 5 6 7 9 8 4 A B C D E F G H I CS Data Structures

In-order Traversal A B C D E F G H I 3 1 2 5 6 7 9 8 4
Algorithm inOrder(v) if left (v) ≠ null inOrder (left (v)) visit(v) if right(v) ≠ null inOrder (right (v)) inOrder(A) inOrder(B) CS Data Structures

Algorithm inOrder(v) if left (v) ≠ null inOrder (left (v)) visit(v) if right(v) ≠ null inOrder (right (v)) inOrder(B) inOrder(D) CS Data Structures

Algorithm inOrder(v) if left (v) ≠ null inOrder (left (v)) visit(v) if right(v) ≠ null inOrder (right (v)) inOrder(D) // no left children visit(D) // 1 // no right children return CS Data Structures

Algorithm inOrder(v) if left (v) ≠ null inOrder (left (v)) visit(v) if right(v) ≠ null inOrder (right (v)) inOrder(B) // left child visited visit(B) // 2 inOrder(E) CS Data Structures

Algorithm inOrder(v) if left (v) ≠ null inOrder (left (v)) visit(v) if right(v) ≠ null inOrder (right (v)) inOrder(E) inOrder(H) CS Data Structures

Algorithm inOrder(v) if left (v) ≠ null inOrder (left (v)) visit(v) if right(v) ≠ null inOrder (right (v)) inOrder(H) // no left children visit(H) // 3 // no right children return CS Data Structures

Algorithm inOrder(v) if left (v) ≠ null inOrder (left (v)) visit(v) if right(v) ≠ null inOrder (right (v)) inOrder(E) // left child visited visit(E) // 4 inOrder(I) CS Data Structures

Algorithm inOrder(v) if left (v) ≠ null inOrder (left (v)) visit(v) if right(v) ≠ null inOrder (right (v)) inOrder(I) // no left children visit(I) // 5 // no right children return CS Data Structures

Algorithm inOrder(v) if left (v) ≠ null inOrder (left (v)) visit(v) if right(v) ≠ null inOrder (right (v)) inOrder(E) // right children visited return CS Data Structures

Algorithm inOrder(v) if left (v) ≠ null inOrder (left (v)) visit(v) if right(v) ≠ null inOrder (right (v)) inOrder(B) // right children visited return CS Data Structures

Algorithm inOrder(v) if left (v) ≠ null inOrder (left (v)) visit(v) if right(v) ≠ null inOrder (right (v)) inOrder(A) // left child visited visit(A) // 6 inOrder(C) CS Data Structures

Algorithm inOrder(v) if left (v) ≠ null inOrder (left (v)) visit(v) if right(v) ≠ null inOrder (right (v)) inOrder(C) inOrder(F) CS Data Structures

Algorithm inOrder(v) if left (v) ≠ null inOrder (left (v)) visit(v) if right(v) ≠ null inOrder (right (v)) inOrder(F) // no left children visit(F) // 7 // no right children return CS Data Structures

Algorithm inOrder(v) if left (v) ≠ null inOrder (left (v)) visit(v) if right(v) ≠ null inOrder (right (v)) inOrder(C) // left child visited visit(C) // 8 inOrder(G) CS Data Structures

Algorithm inOrder(v) if left (v) ≠ null inOrder (left (v)) visit(v) if right(v) ≠ null inOrder (right (v)) inOrder(G) // no left children visit(G) // 9 // no right children return CS Data Structures

Algorithm inOrder(v) if left (v) ≠ null inOrder (left (v)) visit(v) if right(v) ≠ null inOrder (right (v)) inOrder(C) // right child visited return CS Data Structures

Algorithm inOrder(v) if left (v) ≠ null inOrder (left (v)) visit(v) if right(v) ≠ null inOrder (right (v)) inOrder(A) // right child visited return CS Data Structures

Algorithm inOrder(v) if left (v) ≠ null inOrder (left (v)) visit(v) if right(v) ≠ null inOrder (right (v)) Done. CS Data Structures

Level-order Traversal
In a level-order traversal, all nodes at the same depth are visited from left to right, then all the nodes at the next depth are visited, etc. Application: Breadth-first search. 1 Algorithm levelOrder(v) Q ← v while !Q.empty w ← Q.dequeue Q.enqueue(left (w)) Q.enqueue(right (w)) visit(w) A B C F G D E 2 3 4 5 6 7 CS Data Structures

B C F G D E 3 1 2 5 6 7 4 Algorithm levelOrder(v) Q ← v while !Q.empty w ← Q.dequeue Q.enqueue(left (w)) Q.enqueue(right (w)) visit(w) levelOrder(A) Q ← A Q not empty w ← A // Q empty Q.enqueue(B) // Q = B Q.enqueue(C) // Q = B C visit(A) // 1 CS Data Structures

B C F G D E 3 1 2 5 6 7 4 Algorithm levelOrder(v) Q ← v while !Q.empty w ← Q.dequeue Q.enqueue(left (w)) Q.enqueue(right (w)) visit(w) levelOrder(A) Q not empty w ← B // Q = C Q.enqueue(D) // Q = C D Q.enqueue(E) // Q = C D E visit(B) // 2 CS Data Structures

B C F G D E 3 1 2 5 6 7 4 Algorithm levelOrder(v) Q ← v while !Q.empty w ← Q.dequeue Q.enqueue(left (w)) Q.enqueue(right (w)) visit(w) levelOrder(A) Q not empty w ← C // Q = D E Q.enqueue(F) // Q = D E F Q.enqueue(G) // Q = D E F G visit(C) // 3 CS Data Structures

B C F G D E 3 1 2 5 6 7 4 Algorithm levelOrder(v) Q ← v while !Q.empty w ← Q.dequeue Q.enqueue(left (w)) Q.enqueue(right (w)) visit(w) levelOrder(A) Q not empty w ← D // Q = E F G Q.enqueue(null) // Q = E F G Q.enqueue(null) // Q = E F G visit(D) // 4 CS Data Structures

B C F G D E 3 1 2 5 6 7 4 Algorithm levelOrder(v) Q ← v while !Q.empty w ← Q.dequeue Q.enqueue(left (w)) Q.enqueue(right (w)) visit(w) levelOrder(A) Q not empty w ← E // Q = F G Q.enqueue(null) // Q = F G Q.enqueue(null) // Q = F G visit(E) // 5 CS Data Structures

B C F G D E 3 1 2 5 6 7 4 Algorithm levelOrder(v) Q ← v while !Q.empty w ← Q.dequeue Q.enqueue(left (w)) Q.enqueue(right (w)) visit(w) levelOrder(A) Q not empty w ← F // Q = G Q.enqueue(null) // Q = G Q.enqueue(null) // Q = G visit(F) // 6 CS Data Structures

B C F G D E 3 1 2 5 6 7 4 Algorithm levelOrder(v) Q ← v while !Q.empty w ← Q.dequeue Q.enqueue(left (w)) Q.enqueue(right (w)) visit(w) levelOrder(A) Q not empty w ← G // Q = empty Q.enqueue(null) // Q = empty Q.enqueue(null) // Q = empty visit(G) // 7 CS Data Structures

B C F G D E 3 1 2 5 6 7 4 Algorithm levelOrder(v) Q ← v while !Q.empty w ← Q.dequeue Q.enqueue(left (w)) Q.enqueue(right (w)) visit(w) levelOrder(A) Q is empty return CS Data Structures

Traversal Implementation
Pre-order, in-order, and post-order traversals. Big-O time and space? Level-order traversal using a queue. Level-order traversal without a queue. CS Data Structures

Binary Trees A binary tree is a tree with the following properties:
Each internal node has at most two children (exactly two for proper binary trees). The children of a node are an ordered pair. We call the children of an internal node left child and right child. A B C F G D E H I Applications: arithmetic expressions decision processes searching CS Data Structures

Types of Binary Trees full binary tree: A binary tree is which each node was exactly 2 or 0 children. complete binary tree: A binary tree in which every level, except possibly the deepest, is completely filled. At depth n, the height of the tree, all nodes are as far left as possible. perfect binary tree: A binary tree with all leaf nodes at the same depth. All internal nodes have exactly two children. A perfect binary tree has the maximum number of nodes for a given height. G E D B A C F E D B A C F C A F G E D B

Question 1 What is the maximum height of a full binary tree with 11 nodes? 1 3 5 7 Not possible to construct a full binary tree with 11 nodes. CS Data Structures

Question 2 What is the height of a complete binary tree that contains n nodes? 1 log 2 𝑛 𝑛 𝑛 log 2 𝑛 CS Data Structures

Arithmetic Expression Tree
Binary tree associated with an arithmetic expression. internal nodes: operators external nodes: operands Example: Expression tree for this arithmetic expression: (2  (a - 1) + (3  b)) +  - 2 a 1 3 b CS Data Structures

Evaluate Arithmetic Expressions
Specialization of a post-order traversal. Recursive method returning the value of a sub-tree. When visiting an internal node, combine the values of the sub-trees. Algorithm evalExpr(v) if isExternal (v) return v.element () else x  evalExpr(left(v)) y  evalExpr(right(v))   operator stored at v return x  y +  - 2 5 1 3 ((2  (5 - 1)) + (3  2)) = 14 CS Data Structures

Print Arithmetic Expressions
Specialization of an in-order traversal. Print operand or operator when visiting node. Print “(“ before traversing left subtree. Print “)“ after traversing right subtree. Algorithm printExpression(v) if left (v) ≠ null print(“(’’) inOrder (left(v)) print(v.element ()) if right(v) ≠ null inOrder (right(v)) print (“)’’) +  - 2 a 1 3 b ((2  (a - 1)) + (3  b)) CS Data Structures

Decision Tree Binary tree associated with a decision process.
internal nodes: Questions with yes/no answer. external nodes: Decisions Example: Whether to check . CS Data Structures

Example: Decision Tree
What is the maximum of these numbers: x1, x2, x3? x1 > x2 x2 > x3 x1 > x3 x3 x1 x2 No Yes No Yes No Yes CS Data Structures

Properties of Binary Trees
Notation: n - number of nodes e - number of external nodes (leaves) i - number of internal nodes h - height Properties: e = i + 1 n = 2e - 1 h  i h  (n - 1)/2 e  2h h  log2 e h  log2(n + 1) - 1 CS Data Structures

Tree Searches Algorithms for searching tree data structures.
Breadth-First Search: Starting at the root, explores all the nodes on a given level before exploring nodes on the next level. A form of Level-Order Traversal. Usually implemented with a Queue. Depth-First Search: Starting at the root, explores a node one level down. Continues searching, moving down one level down, until reach a leaf. Then backtrack to last node with a non-empty child node and repeat the search. A form of Pre-Order Traversal. Usually implemented with a Stack. CS Data Structures

Breadth-First Search Algorithm BFS(r) // root node of tree
Q.enqueue(r) mark r as visited while(!Q.empty) v ← Q.dequeue for all children w of v if w is not visited Q.enqueue(w) mark w as visited CS Data Structures

Example: Breadth-First Search
8 3 10 1 6 13 4 7 14 CS Data Structures

Depth-First Search Algorithm DFS(r) // root node of tree S.push(r)
mark s as visited while(!S.empty) v ← S.pop for all children w of v if w is not visited S.push(w) mark w as visited CS Data Structures

Example: Depth-First Search
8 3 10 1 6 13 4 7 14 CS Data Structures

Linked Structure for Trees
A node is represented by an object storing: Element Parent node Sequence of children nodes  B A D F C E B D A C E F CS Data Structures

Linked Structure for Binary Trees
A node is represented by an object storing: Element Parent node Left child node Right child node  B A D C E B D A C E CS Data Structures

Array-Based Representation of Binary Trees
3 1 2 5 6 4 9 10 A H G F E D C B J Nodes are stored in an array A A B D G H … 1 2 9 10 Node v is stored at A[rank(v)] rank(root) = 0 If node is the left child of parent(node), rank(node) = 2*rank(parent(node)) + 1 If node is the right child of parent(node), rank(node) = 2*rank(parent(node)) + 2 CS Data Structures

CS Data Structures

Tree ADT We use positions to abstract nodes Generic methods:
integer size() boolean isEmpty() Iterator iterator() Iterable positions() Accessor methods: position root() position parent(p) Iterable children(p) Integer numChildren(p) Query methods: boolean isInternal(p) boolean isExternal(p) boolean isRoot(p) Additional update methods may be defined by data structures implementing the Tree ADT CS Data Structures

Java Interface Methods for a Tree interface: CS Data Structures

BinaryTree ADT The BinaryTree ADT extends the Tree ADT, i.e., it inherits all the methods of the Tree ADT Additional methods: position left(p) position right(p) position sibling(p) The above methods return null when there is no left, right, or sibling of p, respectively Update methods may be defined by data structures implementing the BinaryTree ADT CS Data Structures

Euler Tour Traversal +   2 - 3 2 5 1
Generic traversal of a binary tree Includes a special cases the preorder, postorder and inorder traversals Walk around the tree and visit each node three times: on the left (preorder) from below (inorder) on the right (postorder) + L  R  B 2 - 3 2 5 1 CS Data Structures

Trees "A tree may grow a thousand feet tall, but its leaves will return to its roots." -Chinese Proverb CLRS, Section 10.4.

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Trees "A tree may grow a thousand feet tall, but its leaves will return to its roots." -Chinese Proverb CLRS, Section 10.4.

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Presentation on theme: "Trees "A tree may grow a thousand feet tall, but its leaves will return to its roots." -Chinese Proverb CLRS, Section 10.4."— Presentation transcript:

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